Desribing your data thoroughly and at muliple levels is one of the most important data management practices you can do for both yourself and for others.
Description and documentation is also sometimes referred to as metadata. Metadata is information about your data or processes that helps provide context for understanding your research data. For example, metadata that you capture about your data might include things like when it was collected, who collected it, how it was collected, any instruments used, or other technical information.
Metadata is important to have at the project or folder level as well as at the item or file level. Describing your data at multiple levels helps provide a more complete picture of how the data was produced, gathered, cleaned, and analyzed.
Create a README file. A README file is a plain text file that provides overarching important information the project and files within the project. It should include a suggested minimum amount of information that will contribute to a dataset’s reusability.
What goes in a README file?
Best practices for README files:
If you choose to create a README file, be aware that there is a suggested minimum amount of information to capture about your work to ensure that your data is reusable.
This information includes:
Suppose you’re a botanist who’s leading a team that will count how many specimens of a certain plant are living within an area.
Which of the following are examples of metadata you might record? Select all that apply.
Answer: All of these!
Metadata can include any information that may provide important context later on about the data that you’re collecting.
 Cornell University Research Data Management Service Group, “Guide to writing ‘readme’ style metadata”, https://data.research.cornell.edu/content/readme.